1. Field of the Invention
The present invention relates to systems and methods for analyzing computer generated communications.
2. Description of the Prior Art
Psychological profiling algorithms have been developed based upon the work of Weintraub. Weintraub has identified 14 critical speech categories, set forth in FIG. 1, which are believed by psychologists to reflect the operation of psychological coping mechanisms or defenses. Weintraub's opinion is that the distribution of these variables indicate the distribution of defenses in an individual and provides insight into the individual's psychological state or personality. Weintraub's and his colleague's original research dates from 1964.
This original research demonstrated differences in the distribution of these categories of speech as used by normal persons and persons with different forms of psychopathology, including depression, impulsiveness, delusions and compulsiveness. Weintraub profiled and compared political leaders, such as participants in Watergate in 1981. In 1989, he extended his methodology for leadership profiling to the assessment and comparison of U.S. Presidents, including Eisenhower, Kennedy, Johnson, Nixon, Ford, Carter and Reagan.
Over the past 35 years, Weintraub's algorithms have also been used to analyze the speech and written products of leaders to develop in-depth psychological profiles of these individuals and comparisons between them. Weintraub has also discussed providing computerized portions of his algorithms to expedite the analytical process.
However, Weintraub's algorithms are not known by the public to have been applied to the evaluations of changes in an individual's psychological state over time; the communications of normal employees in the workplace; to computer generated communications, e.g. email and chat; generating a warning of a potentially dangerous change in an individual's psychological state; or self-monitoring of psychological state.
The Weintraub algorithms quantify the number of words in the above-referenced speech categories of FIG. 1. The total word count for each category may be multiplied by a corrective figure which is obtained by dividing 1,000 by the number of words in the sample and rounding off to three decimal places to provide a normalized basis for comparison.
The Weintraub algorithms may be used to profile the following psychological states:
1. Indicators of Anger—
Increases in the number of:                words;        personal references;        negatives;        evaluators;        statements of feeling;        direct references;        rhetorical questions;        interruptions;        I; and        We        
Decreases in the number of:                qualifiers; and        retractors.        
2. Indicators of Anxiety—                —Increases in the number of:        retractors;        qualifiers;        expressions of feeling;        negatives; and        explainers.        
3. Indicators of Depression—                decreased number of words        increased I        increased me        increased negative key words        increased direct references        increased expressions of feeling key words        increased evaluators        increased adverbial intensifiers        
4. Indicators of Emotional Withdrawal—                decreased number of words;        decreased number of communications;        decreased I score;        decreased personal references;        decreased expressions of feelings; and        decreased evaluators.        
5. Indicators of Rigidity or Lack of Flexibility—                decreased number of qualifiers;        decreased number of retractors;        decreased we's;        increased I's;        decreased explainers;        increased evaluators; and        increased adverbial intensifiers.        
6. Indicators of Impulsiveness—                increased retractors and        increased expressions of feeling.        
7. Indicators of Emotional Instability—                increased I-to-We ratio;        increased adverbial intensifiers        increased direct references        increased expression of feelings        increased evaluators.        
Score Interpretations of Weintraub's psychological profiling algorithms have been suggested as follows:
1. I Scores:                high I score—self-preoccupied        moderate I—healthy ability to commit self in thought and action while maintaining degree of autonomy;        low I—avoidance of candor, intimacy, commitment.        
2. We Scores:                moderate score—healthy capacity to recognize and collaborate with others        high we+low I—avoidance of intimacy and commitment.        
3. Me:                high use reflects dependence and passivity.        
4. Negatives:                high scores associated with stubbornness, opposition, anger, use of denial as defense mechanism.        
5. Qualifiers                low score—dogmatism—over-certainty, rigidity.        high score—lack of decisiveness, avoidance of commitment        very high score—anxiety        
6. Retractors                high score—difficulty adhering to previous decisions, impulsiveness        moderate—mature capacity to reconsider, flexibility, openness to new possibility.        very low—dogmatism, rigidity.        
7. Direct References                high scores—difficulty with correspondence or conversation, seeking to distract or manipulate        low or absent—shyness, aloofness, anxiety        
8. Explainers                high—use of rationalization        low or absent—dogmatism, rigidity        
9. Expressions of Feeling                low score—aloofness, hesitant to share feelings, trust        high score—insincere, histrionic        
10. Evaluators                high scores—severe or troubled conscience, psychopathology, anger, dogmatism, rigidity        Low scores—fear of intimacy, lack of commitment        
11. Adverbial Intensifiers                high scores indicate histrionic personality, exaggeration, rigidity, judgmental        
12. Rhetorical Questions—Increase Anger and an effort to control the dialogue
13. Interruptions—Increased Anger and an Effort to Dominate
The specialized composite scores with relevance for personal relationships, organizational behavior and leadership remain unpublished but include:                emotionally controlled—low anxiety and depression scores        sensitivity to criticism—high negatives+high explainers+high I+me.        accommodating versus rivalrous-low to moderate negatives and moderate to high retractors        oppositional-high negatives score.        controlling in relationships—low score on negatives, feelings, evaluators, and qualifiers.        passive vs. active—high me score.        planner vs. reactor—high I+we: me ratio.        decisiveness—low to moderate qualifiers.        unrealistic—high negatives.        high need for others—high we.        high need for achievement—high I+We, low me, low qualifiers.        dependent-high me plus high evaluators, negatives, feelings.        well organized—high I+we, low me, low qualifiers, low evaluators, ow feelings, low negatives.        narcissistic—high negatives+high explainers+high evaluators, high I, low qualifiers.        obsessive—high evaluators+high negatives+low retractors, low me, low qualifiers, low feelings        paranoid—high negatives, high explainers, low retractors.        loner vs. team player—high I, low we or I:We.        
Beginning in the late 1950's, Gottschalk demonstrated that the arousal associated with psychological events plays an important role in the occurrence of epileptic seizures in children and later (1955) in adults. While working at the National Institute of Mental Health, Gottschalk and his colleagues explored differences in the effects of different forms of stimulation on speech variables, such as rate, frequency, duration of pauses, grammatical categories and parts of speech (Gottschalk and Hambridge, 1955). Later, Gottschalk and his colleagues examined differences in speech between psychotic and non-psychotic patients (Gottschalk, Glessner and Hambridge, 1957). In 1958, Gottschalk conducted a time series analysis of the verbal behavior of a single psychoanalytic patient to determine any possible effects of the therapy (Hambridge and Gottschalk, 1958).
In the 1960's, Gottschalk worked with Dr. Golding Glenser at the University of Cincinnati. This work identified variations in the use of parts of speech by normal individuals according to gender and intelligence (for example, Gleser, Gottschalk and John 1959; Gottschalk and Gleser, 1964). Gottshalk and Gleser (1960) also used their content analysis method to distinguish genuine from pseudo-suicide notes. By the end of the 1960's, Gotschalk and his colleagues added new complexity to their content analysis method by moving from the analysis of individual words to more complex phrases. In 1969, Gottshalk and Gleser described a method for determining an individual's psychological state (anxiety, hostility, alienation, and disorganization) from brief samples of speech (Gottshalk and Gleser 1969). Gottschalk, Wingate and Glesner (1969), have described their content analysis scales in a scoring manual. Since 1969, Gottschalk and colleagues have applied their methods to the study of medical conditions, medications, treatment, and psychological conditions on children and adolescents and adults. This work has been summarized in Gottschalk (1995).
Gottshalk and his colleagues have computerized their content analytical scales in order to make them more efficient and more widely available to other researchers. These efforts are also described in Gottschalk (1995, pgs. 157-160).
Gottschalk and his colleagues have produced a content analytical system that can detect emotional states and changes in emotional states in individuals as a result of a wide range of psychological and medical conditions and treatments. The have also measured changes in these states in individuals over time and designed a computerized version of the system.
However, Gottschalk and his colleagues have not utilized their algorithms regarding communications of normal employees in the work place, computer generated communications, e.g. email and chat, the generation of a warning of a potentially dangerous change in an individual's psychological state or self-monitoring of a psychological state.
Margaret Hermann, over the last 25 years, has used content analysis for psychological profiling. In 1977, Herman (with Thomas Milburn) edited an academic collection entitled “A Psychological Examination of Political Leaders”, (New York Free Press 1977). This text brought together the work of psychologists and political scientists interested in the remote assessment of leadership characteristics utilizing content analysis of the leader's speech and writings. It also contains chapters by political-psychological profilers on the history and different approaches to political psychological content analysis, including Value Analysis (White 1951), Evaluation Assertion Analysis (Osgood 1959), the Psychologic (Shneidman 1961, 1963), General Inquirer (Stone, Dunphy, Smith and Ogilvie 1966), and Mode of Imagery (Winter 1973). Hermann, in 1977, in a chapter entitled, “Vocal Behavior of Negotiators in Periods of High and Low Stress: the 1965-1966 New York City Transit Negotiations,” described a content analytical system that analyzed the psychological state of political leaders involved over a time and in different stress states. The collection of content analytical measures drew on the previous work of psychologists, political scientists and others interested in the assessment of emotional states and their changes over time. In another chapter in the same text, she described three content analysis systems designed to assess a leader's beliefs, motives, decision-making and interpersonal style as it might affect their attitude toward foreign aid. These personal characteristics included optimism, cognitive complexity, and humanitarian ideology. The results of the study related variation in these characteristics to the policy positions taken by the leaders examined. Both Herman and her colleagues have refined and expanded the number of personal characteristics derived from content analysis of a leader's speeches or interviews and detailed their effects on a leader's foreign policy orientation and likely political behavior. The personal characteristics of nationalism, belief in one's ability to control events, need for power, need for affiliation, conceptual complexity, self-confidence, distrust of others, and task orientation, have been applied to over 100 domestic and foreign political leaders, including heads of states and leaders of revolutionary and terrorist organizations.
Hermann uses scores obtained on a leader for each of the aforementioned eight personal characteristics and uses them to classify the leader in terms of six possible foreign policy orientations, including expansionist, active independent, influential, opportunist, mediator and developmental. Each of the orientation types can be expected to differ in world view, political style, decision-making process manner of dealing with political rivals, and in view of foreign policy.
Hermann has designed computerized approaches to her content analytical system. However, complexity of coding required to produce measures for many of the characteristics have limited validity and reliability of the resultant automated process.
In summary, Hermann has designed a content analysis system to assess the motives, beliefs, decision-making and interpersonal style of political leaders. She has applied this system to the in-depth profiling of subjects, comparison with other leaders, and the assessment of the dynamics of leadership groups determined by member differences. She has also used the system to analyze a leader's reaction to distress.
However, Herman has not applied her system to the communications of normal employees in the work place, computer generated communications, e.g. email and chat; the task of generating a warning of a potentially dangerous change in an individual's psychological state; or self-monitoring of a psychological state.
Another measure of psychological state is described in Mehrabian and Wiener (1966) which is identified herein as “Psychological Distance”. Psychological distance is an emotional state expressed by the speaker toward a target, individual or group. Because the speaker normally unconsciously selects the semantic structures used to calculate psychological distance, it is an excellent measure of “covert” attitude. When a speaker's covert attitude, as measured by psychological distances, is compared with overt content of a speaker's remarks (the number of negative, positive or neutral words associated with the name of an individual or group), it becomes a reliable measure of deception or bluffing. For example, if the overt attitude toward the person or group is positive and the covert attitude is negative, this is an indicator of deception. If the covert attitude towards the group or individual is more positive than the overt attitude, this is an indicator of bluffing.
Psychological distance is scored according to the following guidelines. First, each reference by the speaker to the target is identified. Second, the word structures around the reference to the target are evaluated for the presence or absence of each of the nine conditions below. Third, for each time one of these nine conditions is present, a single score is received. Fourth, for each communication, an average psychological distance score is constructed by taking the number of references to the target divided by the number of points received in the communication across all references to the target. This score is usually between one and nine with the higher score indicating the presence of greater hostility or psychological distance.
Psychological Distance Coding Guideline
1. Spatial: the communicator refers to the object of communication using demonstrative pronouns such as “that” or “those.” E.g. “those people need help” versus “these people need help.”
2. Temporal: the communicator's relationship with the object of communication is either temporally past or future. E.g., “X has been showing me his house” versus “X is showing me his house.”
3. Passivity: the relationship between the communicator and the object of communication is imposed on either or both of them. E.g., “I have to see X” versus “I want to see X.”
4. Unilaterally: the relationship between communicator and the object of communication is not mutually determined. E.g., “I am dancing with X” versus “X and I are dancing.”
5. Possibility: the relationship between the communicator and the object of communication is possible rather than actual. E.g., “I could see X” versus “I want to see X.”
6. Part (of Communicator): only a part, aspect, or characteristic of the communicator is involved in the relationship with the object of communication. E.g., “My thoughts are about X” versus “I am thinking of X.”
7. Object (Part of Object): only a part, aspect, or characteristic of the object of communication is involved in the relationship with the communicator. E.g., “I am concerned about X's future” versus “I am concerned about X.”
8. Class (of Communicator): a group of people who include the communicator is related to the object of communication. E.g., “X came to visit us” versus “X came to visit me.”                9. Class (of Object): the object of communication is related to as a group of objects, which includes the object of communication, e.g., “I visited X and his wife” versus “I visited X.”        
In December 1999, at pages 43-44, in Security Management, it was stated:                “The [inventor's] firm, has developed psycholinguistic measures sensitive to changes in an employee's psychological state indicative of increased risk. In the case of the employee who abruptly changes tone in his email messages, post hoc use of these measures detected both the employee's initial disgruntlement and the contrast between his overt and covert activities. Had these automated measures been monitored by security, this incident might have been prevented”.        
FIGS. 2-5 illustrate slides presented by the inventor at conferences on May 12, 1999, Jun. 17, 1999, Jul. 28, 1999, and Oct. 20, 1999 to persons involved with the security industry which analyzed the electronic mail messages of an actual perpetrator of a computer crime. The mean prior values of the number of “negatives”, as illustrated in FIG. 2, the number of “evaluators” as illustrated in FIG. 3, the “number of words per email”, as illustrated in FIG. 4, and the “number of alert phrases” as illustrated in FIG. 5 were compared to the values obtained from analysis of an electronic mail message prior to and associated with the crime in question. The increase over the mean values was discussed as indicating the risk of the criminal activity in question. The slides of FIGS. 2-5 represent the inventor's analytical analysis after the crime occurred of emails of the perpetrator of the crime in question and were not produced at the time of the crime or at the time of the conferences by the present invention.
FIG. 6 illustrates a slide presented by the inventor at the aforementioned conferences analyzing continued covert hostility versus psychological distance over time. As time passed, the criminal whose activities are analyzed above in FIGS. 2-5, deceived his supervisor with “charming pleasantries” as the attack was prepared. Prior art email screening techniques would also have been deceived by the activities of the criminal. As is shown in FIG. 6, a continued high degree of psychological distance was exhibited in emails after the plan of the attack was occurring. This graph was produced by the analytical analysis of the author, was not produced by an analysis of the criminal's activity as events unfolded and was not produced with the present invention.
FIG. 7 illustrates another slide provided by the inventor at the aforementioned conferences illustrating indicators of psychological distance versus overt attitude consistent with deception. Again, as is seen, the aforementioned conduct of the prior art of FIGS. 2-6 show a drop in overt hostility from three months to two weeks prior to the crime which deceived the criminal's supervisor while the analysis, as depicted in FIG. 6, shows a more or less constant continued covert hostility. The graph of FIG. 7 was produced by the inventor's analytical analysis and was not produced with the present invention.
Email-monitoring software for the securities industry has been developed as a result of a SEC order that brokerage houses monitor their sales force for illegal sales practices. This software detects key words indicative of potential trading sales violations.
As a result of increased employee use of information technology, non-psychological systems of employee monitoring have emerged which are designed to protect companies from employee misuse or other threats. These systems are operated by companies to monitor employee use of information technology to detect patterns involving unauthorized visits to internet sites; errors in the use of software requiring additional training; and visits by email or other communications to or from unauthorized sites within and external to the organization.
In addition, systems exist to detect occurrence of “keywords” indicative of possible violations of law (the above-referenced security industries practice) and regulations or the existence of possible security violations.
Other systems screen incoming and outgoing communications for the existence of dangerous viruses and/or other destructive content. However, none of these systems currently assesses the psychological state of an employee to generate an indicator of risk.